With the increase in mobile computing devices and wireless LANs, it has become important to determine the position of a device at any point in time. The increasing popularity of Institute of Electrical and Electronics Engineers (IEEE) standard 802.11 conforming hardware and networks make it a particularly attractive implementation platform for a wireless positioning system. Due to its widespread adoption, it is easy to obtain commercial off the shelf conforming hardware. Further, many mobile computing devices already have conforming equipment installed, eliminating the need to purchase and install additional hardware. Likewise, the prevalence of networks conforming to the IEEE 802.11 specification provides a pre-existing infrastructure for wireless positioning.
Unfortunately, commercial off the shelf IEEE 802.11 conforming hardware does not provide positioning functionality. Despite this, positioning can be implemented by using received signal strength measurements provided by an IEEE 802.11 conforming wireless card.
There are two common approaches to solve the problem of locating a wireless client device. The received signal strength indicator (RSSI) is a measure of the power received by the client from an access point (AP) and provides information as to the position of the client. Indeed, RSSI is position dependent as it is affected by factors such as distance from the access point and attenuation due to intervening walls.
In a first method, a database of RSSI measurements of one or more known access points (APs) is built and the database of RSSI information of the APs is used by a wireless mobile client to determine its own position. In the first method, a radio map, also known as a radio fingerprint, comprises a database of RSSI vector measurements associated with corresponding positions. The wireless mobile client device determines its own position using the RSSI vector measurement and various classification techniques. Radio map algorithms require a burdensome data collection phase where a large number of signal strength measurements must be recorded along with the corresponding position. If access points are moved, or the environment changes, the radio map is no longer valid and the data collection process must be repeated.
In a second method, signal propagation is modeled by taking into consideration obstructions in the signal propagation path, such as walls, furniture, and other objects in the environment. The signal propagation model is then used by the wireless mobile client to compute expected signal strength. Such an approach is rather unlikely to work in practice, except in some ideal or highly simplistic cases, primarily attributed to the complexity involved in accurately modeling signal propagation inside structures, like an office building.
Thus, both prior art methods require some field surveying to build the RSSI database and radio map or to calibrate the signal propagation model. A wireless client present at the actual position is used to take readings, which could be inaccurate due to the huge subset of various mobile wireless client device vendors with numerous differing technical specifications concerning generation of RSSI, and the resultant variation in RSSI readings generated by this plethora of devices.
Further, both methods require installation of software and data on the wireless mobile client so that it can determine its own position.
Therefore, there is a need for an improved method of determining the position of a mobile wireless client device without such constraints.